Research Article
Attacker Capability based Dynamic Deception Model for Large-Scale Networks
@ARTICLE{10.4108/eai.13-7-2018.162808, author={Md Ali Reza Al Amin and Sachin Shetty and Laurent Njilla and Deepak K. Tosh and Charles Kamhoua}, title={Attacker Capability based Dynamic Deception Model for Large-Scale Networks}, journal={EAI Endorsed Transactions on Security and Safety}, volume={6}, number={21}, publisher={EAI}, journal_a={SESA}, year={2019}, month={8}, keywords={cyber deception, network security, POMCP, POMDP, SDN, exploit dependency graph}, doi={10.4108/eai.13-7-2018.162808} }
- Md Ali Reza Al Amin
Sachin Shetty
Laurent Njilla
Deepak K. Tosh
Charles Kamhoua
Year: 2019
Attacker Capability based Dynamic Deception Model for Large-Scale Networks
SESA
EAI
DOI: 10.4108/eai.13-7-2018.162808
Abstract
In modern days, cyber networks need continuous monitoring to keep the network secure and available to legitimate users. Cyber attackers use reconnaissance mission to collect critical network information and using that information, they make an advanced level cyber-attack plan. To thwart the reconnaissance mission and counterattack plan, the cyber defender needs to come up with a state-of-the-art cyber defense strategy. In this paper, we model a dynamic deception system (DDS) which will not only thwart reconnaissance mission but also steer the attacker towards fake network to achieve a fake goal state. In our model, we also capture the attacker’s capability using a belief matrix which is a joint probability distribution over the security states and attacker types. Experiments conducted on the prototype implementation of our DDS confirm that the defender can make the decision whether to spend more resources or save resources based on attacker types and thwart reconnaissance mission.
Copyright © 2019 Md Ali Reza Al Amin et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.